| id |
ecaade2020_047 |
| authors |
Brown, Lachlan, Yip, Michael, Gardner, Nicole, Haeusler, M. Hank, Khean, Nariddh, Zavoleas, Yannis and Ramos, Cristina |
| year |
2020 |
| title |
Drawing Recognition - Integrating Machine Learning Systems into Architectural Design Workflows |
| doi |
https://doi.org/10.52842/conf.ecaade.2020.2.289
|
| source |
Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 289-298 |
| summary |
Machine Learning (ML) has valuable applications that are yet to be proliferated in the AEC industry. Yet, ML offers arguably significant new ways to produce and assist design. However, ML tools are too often out of the reach of designers, severely limiting opportunities to improve the methods by which designers design. To address this and to optimise the practices of designers, the research aims to create a ML tool that can be integrated into architectural design workflows. Thus, this research investigates how ML can be used to universally move BIM data across various design platforms through the development of a convolutional neural network (CNN) for the recognition and labelling of rooms within floor plan images of multi-residential apartments. The effects of this computation and thinking shift will have meaningful impacts on future practices enveloping all major aspects of our built environment from designing, to construction to management. |
| keywords |
machine learning; convolutional neural networks; labelling and classification; design recognition |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (465,508 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:54 |
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